Title: Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics

Abstract

Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liquid chromatography-electrospray ionization-mass spectrometry. Here, we introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. Our algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation analysis of a total of 140 metabolites across three different sets of biological samples analyzed with liquid chromatography-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral molecular masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scoresmore » take into account the number of in-source fragments matched and the relative intensity similarity between the experimental data and the reference low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu.« less

Authors:

Domingo-Almenara, Xavier  ^[1];

• Search DOE PAGES for author "Domingo-Almenara, Xavier"
• Search DOE PAGES for ORCID "0000-0002-0133-6863"

Montenegro-Burke, J. Rafael  ^[1];

• Search DOE PAGES for author "Montenegro-Burke, J. Rafael"
• Search DOE PAGES for ORCID "0000-0001-7787-3414"

Guijas, Carlos  ^[1];

• Search DOE PAGES for author "Guijas, Carlos"
• Search DOE PAGES for ORCID "0000-0001-7993-3388"

Majumder, Erica L.-W. ^[1]; Benton, H. Paul ^[1]; Siuzdak, Gary  ^[1]

• Search DOE PAGES for author "Siuzdak, Gary"
• Search DOE PAGES for ORCID "0000-0002-4749-0014"

---

+ Show Author Affiliations

1. The Scripps Research Inst., La Jolla, CA (United States)

Publication Date:

Fri Jan 25 00:00:00 EST 2019

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institutes of Health (NIH)

OSTI Identifier:

1777377

Grant/Contract Number:  

AC02-05CH11231; P01DA026146-02; P30MH062261-17; R01GM114368-03

Resource Type:

Accepted Manuscript

Journal Name:

Analytical Chemistry

Additional Journal Information:

Journal Volume: 91; Journal Issue: 5; Journal ID: ISSN 0003-2700

Publisher:

American Chemical Society (ACS)

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; mass spectrometry; adducts; peptides and proteins; metabolism; monomers